4.5 Article

Investigation of thermophysical properties of synthesized SA and nano-alumina reinforced polyester composites

期刊

PETROLEUM SCIENCE AND TECHNOLOGY
卷 41, 期 23, 页码 2173-2189

出版社

TAYLOR & FRANCIS INC
DOI: 10.1080/10916466.2022.2108838

关键词

Nano-alumina; polyester composite; shore D hardness; thermal conductivity; thermal decomposition

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In this study, a novel organic compound with a special structure was synthesized and used to reinforce polyester composites. The properties of the composites were investigated through experiments and characterization. The results showed that nano-alumina filler could enhance the thermal conductivity and hardness of the composites, while the synthesized SA had the opposite effect.
Synthesis and characterization of 4-[(E)-(5-bromo-2-hydroxybenzylidene) amino]-N-(4-methylpyrimidin-2-yl) benzenesulfonamide (SA) has been performed. Alumina (A1 2 0 3 ) and SA reinforced polyester composites are synthesized, and characterization processes are carried out. SA formed has been characterized by Fourier transform infrared (FTIR) spektrofotometre and nuclear magnetic resonance (NMR) spectroscopy. The thermal decomposition behavior of the nanocomposites in a nitrogen environment under non-isothermal conditions from 298 to 973 K is investigated with proportional-integral-derivative (P1D) system. Thermal conductivity, Shore D hardness, and thermal decomposition behaviors of the nanocomposites that are added with alumina filler and synthesized with SA reinforcement have been compared. Nano-alumina filler raises the thermal conductivity coefficient and Shore D hardness of the polyester composites. The addition of synthesized SA reduces both the thermal conductivity coefficient and Shore D hardness. The thermal conductivity coefficient has been measured at the lowest in the pure polyester (0.056 W/m.K), and highest in the nano-alumina reinforced composite (0.072 W/m.K). The lowest Shore D hardness (nearly 68) is determined in the synthesized SA reinforced composite. Also, the experimental study has been optimized with the help of response surface methodology (RSM), and the improved theoretical models have been evaluated by statistical analysis. [GRAPHICS] .

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